Systems and Methods for Quantifying Multiscale Competitive Landscapes of Clonal Diversity in Glioblastoma

ABSTRACT

Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme (“GBM”) are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/635,276, filed on Feb. 26, 2018, and entitled“QUANTIFYING MULTISCALE COMPETITIVE LANDSCAPES OF CLONAL DIVERSITY INGLIOBLASTOMA,” which is herein incorporated by reference in itsentirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under CA220378 awardedby the National Institutes of Health. The government has certain rightsin the invention.

BACKGROUND

Glioblastoma (“GBM”) exhibits profound intratumoral molecularheterogeneity that contributes to treatment resistance and poorsurvival. Each tumor contains multiple molecularly-distinctsubpopulations with different treatment sensitivities. Thisheterogeneity not only supports the pre-existence of resistantsubpopulations, but also communication between subpopulations thatfurther modulates tumorigenicity and resistance.

For instance, a minority tumor subpopulation with EGFRvIII mutation hasbeen shown to potentiate a majority subpopulation with wild-type EGFR,increasing tumor growth, cell survival, and drug resistance. Thiscooperativity presents implications for improving GBM treatment. Yet,compared to other tumor types, the interactions in GBM remainunderstudied, particularly among common GBM driver gene alterations.

Tissue sampling is a significant barrier to studying the interactionsbetween molecularly-distinct subpopulations in GBM. Notably,contrast-enhanced MRI (“CE-MRI”) routinely guides surgical biopsy andresection of the MRI enhancing core, but fails to address the diversesubpopulations of the surrounding non-enhancing parenchyma (so called“brain around tumor” or “BAT”). These unresected residual subpopulationsin BAT represent the main contributors to tumor recurrence and canexhibit different therapeutic targets compared with enhancing biopsies.

SUMMARY OF THE DISCLOSURE

The present disclosure addresses the aforementioned drawbacks byproviding a method for constructing and implementing a machine learningmodel to generate at least one image that depicts spatial patterns of amolecular marker across a region-of-interest in a subject. One or moretrained machine learning models are constructed by accessing trainingdata with a computer system, quantifying regional molecular diversity inone or more subjects from the training data, and training one or moremachine learning models based on the training data and the quantifiedregional molecular diversity in the one or more subjects. The trainingdata include magnetic resonance imaging (“MRI”) data acquired from theone or more subjects and molecular data determined from biopsiescollected from the one or more subjects, wherein the molecular datacomprises DNA copy number variation (“CNV”) data and exome data. Eachmachine learning model is trained on the training data to localizemolecularly distinct subpopulations and phenotypic niches across aregion-of-interest. An image that depicts spatial patterns of amolecular marker across a region-of-interest in a subject is thengenerated by inputting magnetic resonance images acquired from thatsubject to the appropriately trained machine learning model (i.e., amachine learning model that has been trained for the molecular marker ofinterest).

It is another aspect of the present disclosure to provide a method forconstructing and implementing a machine learning model to generate atleast one image that depicts spatial patterns of a biomarker across aregion-of-interest in a subject. The method includes constructing one ormore trained machine learning models by accessing training data with acomputer system, quantifying regional biomarker diversity in one or moresubjects from the training data, and training one or more machinelearning models based on the training data and the quantified regionalbiomarker diversity in the one or more subjects. The training datainclude imaging data acquired from the one or more subjects andbiological feature data determined from biopsies collected from the oneor more subjects. Each machine learning model is trained on the trainingdata to localize distinct biomarker subpopulations across aregion-of-interest. An image that depicts spatial patterns of abiomarker across a region-of-interest in a subject is then generated byinputting images acquired from that subject to the appropriately trainedmachine learning model.

It is still another aspect of the present disclosure to provide a methodfor constructing and implementing a machine learning model to generateat least one image that depicts spatial patterns of spatially resolveddata across a spatial or geographical region. The method includesconstructing one or more trained machine learning models by accessingtraining data with a computer system and training the one or moremachine learning models based on the training data. In general, thetraining data include spatial data acquired from one or more spatialregions and localized data collected from spatial samples in the one ormore spatial regions. Each machine learning model is trained on thetraining data to quantify spatial patterns of localized data across aspatial region. Thus, one or more images that depict spatial patterns oflocalized data across a spatial region are generated by inputtingspatially resolved data acquired from the spatial region to the one ormore trained machine learning models.

The foregoing and other aspects and advantages of the present disclosurewill appear from the following description. In the description,reference is made to the accompanying drawings that form a part hereof,and in which there is shown by way of illustration a preferredembodiment. This embodiment does not necessarily represent the fullscope of the invention, however, and reference is therefore made to theclaims and herein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart setting forth the steps of an example method forconstructing and implementing trained machine learning models that aretrained to generate maps depicting spatial patterns of molecular markersand to quantify regional molecularly distinct subpopulations of tumorsand/or regions around tumors

FIG. 2 is an example workflow implementation of the method of FIG. 1.

FIG. 3 shows an example where regional molecular combinations exhibitdifferent recurrent patterns in a single GBM patient.

FIGS. 4A and 4B show an example of spatial anti-correlation betweenmodel-predicted EGFR amplified (amp) and PDGFR amp subpopulations.

FIG. 5 shows an example outline of a methodology for addressingmulti-scale spatial heterogeneity and temporal dynamics using thesystems and methods described in the present disclosure.

FIG. 6 is an example showing regional differences in response to EGFRinhibition therapy. These results correlate with predictive maps ofwhether a tumor region is EGFR amplified (i.e., sensitive to EGFRinhibitor therapy) or non-amplified (i.e., insensitive to therapy.

FIG. 7 is a block diagram of an example molecular marker map generatingsystem that can be implemented to construct and implement trainedmachine learning models that are trained to generate maps depictingspatial patterns of molecular markers and to quantify regionalmolecularly distinct subpopulations of tumors and/or regions aroundtumors.

FIG. 8 is a block diagram showing examples of hardware components thatcan implement the molecular marker map generating system of FIG. 7.

FIG. 9 is a block diagram of an example MRI system that can beimplemented to acquire MRI data.

DETAILED DESCRIPTION

Described here are systems and methods that implement image-guidedtissue analysis, MRI-based computational modeling, and imaginginformatics to analyze the diversity and dynamics ofmolecularly-distinct subpopulations and the evolving competitivelandscapes in human glioblastoma multiforme (“GBM”) or in other tumortypes. The interactions between regional molecularly-distinctsubpopulations modulate local tumor behavior and regional tumorprogression rates. The systems and methods described in the presentdisclosure construct and implement MRI-based models that cannon-invasively quantify this regional molecular diversity and theinteractions that modulate progression in GBM, including in the brainaround tumor (“BAT”) regions.

Imaging techniques can be used to quantitatively characterize tumors intheir entirety, including unresected BAT regions. The systems andmethods described in the present disclosure use multi-parametric MRI andimage-guided biopsies to develop and validate machine learning models ofintratumoral heterogeneity, with particular focus on the BAT zone. TheseMRI-based predictive models can then be stored and used to quantify theintratumoral diversity of molecularly-distinct subpopulations in humanGBM by inputting MRI data and other relevant data into the trainedpredictive models from subjects whose data were not used to build thepredictive models.

The systems and methods described in the present disclosure are able toquantify intratumoral diversity in residual tumor populations in thenon-enhancing BAT zones of GBM. Tumor populations in the BAT are rarelysurgically biopsied or resected, yet they represent the primary targetsof adjuvant therapy and the main sources of recurrent disease.Quantifying the interactions between molecularly distinct subpopulationsthat are unique to this poorly understood tumor segment will help informfuture strategies (e.g., adaptive therapy) that target the source oftumor recurrence.

The quantification of molecular and subpopulation diversity provided bythe systems and methods described in the present disclosure can beachieved across different spatial scales. Because the predictive modelsare built on spatially-accurate tissue correlations, the MRI-basedmodels can be extended from single biopsy locations to predict regionaldiversity across all tumor subregions, including at the intra-biopsymicroscopic scale, the regional macroscopic scale, and the dynamic tumorsystem scale.

Furthermore, the systems and methods described in the present disclosurecan be used to quantify the temporal dynamics of molecularly-distinctsubpopulation cooperativity and competition over time. The MRI-basedpredictive models can be applied to serial magnetic resonance imagesover time to track how common clonal interactions influence tumorbehavior, treatment response, and recurrence risk over the course ofclinical therapy.

BAT subpopulations represent the primary targets of adjuvant therapy andthe main contributors to tumor recurrence. Using MRI to quantifyintratumoral heterogeneity and the diversity of molecularly-distinctsubpopulations in the BAT can help improve future treatment strategies(e.g., adaptive therapy), particularly under the paradigm ofindividualized oncology. Previous radiogenomic correlative studies haveassigned a single MRI “signature” and genomic profile for an entiretumor, which poorly informs of genomic diversity and spatialheterogeneity within GBM. In contrast, the systems and methods describedin the present disclosure identify imaging “signatures” unique toindividual voxels in each tumor and BAT zone, drastically increasingspatial resolution to better characterize regional molecular diversity.

In general, the systems and methods described in the present disclosureinclude collecting image-recorded stereotactic biopsies and quantify theintra-biopsy heterogeneity of molecular markers of interest. Thesebiopsies can be integrated with spatially matched MRI signatures toinform machine learning models that predict spatial patterns ofmolecular markers across all subregions for the entire tumor. Potentialcooperation and competition dynamics can be identified from co-incidenceanalysis using biopsies and machine learning generated maps. As anexample, predicted temporal dynamics of cooperation and competition canbe quantified.

It is therefore one aspect of the present disclosure to provide systemsand methods for quantifying regional co-existing molecularly-distinctsubpopulations and phenotypic niches across the BAT zone of primary GBM.As will be described, GBM tumors can be evaluated at different levels ofmolecular analysis, such as DNA copy number variants (“CNV”), genemutation (exome), RNA sequencing (“RNA-seq”), and gene expression.

It is another aspect of the present disclosure to provide systems andmethods for implementing MRI-based machine learning models that localizeco-existing molecularly-distinct subpopulations and phenotypic nichesacross BAT. Exome data and intra-biopsy heterogeneity can be used torefine MRI-based machine learning models of regional genomic (e.g., CNV)heterogeneity. Transcriptome (e.g., RNA-seq) data can be used to buildMRI-based models of regional phenotype (e.g., hypoxia, proliferation,angiogenesis, invasion). MRI-based models can be used to generatepredicted maps over the entire BAT regions, and to localize co-existingmolecularly-distinct subpopulations and phenotypic niches. MRI-basedmodels can also be used to quantify combinations of regionalmolecularly-distinct subpopulations and phenotypic niches thatco-localize with regional recurrence in follow-up imaging. As will bedescribed, the predictive models can also be constructed based onbiological factor data, such as sex and age. For instance, sex-specificpredictive models can be constructed to provide increased predictiveaccuracy based on the modeling of sex-based differences.

The systems and methods described in the present disclosure haveadvantageous application to translation studies. The competition andcooperation of molecularly-distinct tumor subpopulations and phenotypicniches can be quantified within the zone of GBM that universally recurs.By providing image-based models that identify the synergisticcombinations of molecular subpopulations that are “at risk” for tumorrecurrence, the systems and methods described in the present disclosurecan elucidate future diagnostic approaches to risk stratify patientsbased on predicted response in targeted clinical drug trials (e.g., GBMAgile trial). The systems and methods described in the presentdisclosure can also facilitate new strategies (e.g., adaptive therapy)to exploit clonal co-dependency for therapeutic benefit.

Each GBM can include multiple molecularly-distinct subpopulations thatinfluence treatment sensitivity and local phenotype. This moleculardiversity fosters the pre-existence of resistant subpopulations, butalso promotes interactions between neighboring subpopulations thatfurther modulate tumorigenicity and resistance. For instance, GBMs mayexhibit heterogeneous expression of receptor tyrosine kinase (“RTK”)aberrations, such as amplification (amp) of epidermal growth factorreceptor (“EGFR”) and platelet-derived growth factor receptor A(“PDGFRA”).

Some studies have shown, for example, that a minority tumorsubpopulation expressing EGFRvIII could potentiate a majoritysubpopulation expressing wild-type EGFR to increase tumor growth, cellsurvival, and drug resistance. Other studies have observed cooperativityamong heterogeneous subpopulations expressing either EGFRamp orPDGFRAamp, such that combined inhibition of both kinases was needed toattenuate phosphoinositide 3-kinase (“PI(3)K”) pathway activity invitro. These interactions between subpopulations create challenges forcurrent treatment paradigms and clinical trials focusing on single drugtargets (e.g., EGFR).

It would also be advantageous to quantify the tumor-tumor interactionsfor other common GBM driver aberrations (e.g., c-MET, PTEN, CDKN2A).Curating these interactions will facilitate promising new approaches(e.g., adaptive therapy) to exploit the codependency and/or competitionamong molecularly-distinct subpopulations for therapeutic benefit.

Contrast-enhanced MRI (CE-MRI) represents the clinical standard forneuronavigation and routinely guides surgical biopsies from the MRIenhancing tumor segment. Unfortunately, biopsies from enhancing tumorregions fail to address the diverse molecularly-distinct subpopulationsthat extend beyond the enhancement into surrounding non-enhancingparenchyma (so called “brain around tumor” or BAT). These residual BATsubpopulations can exhibit different therapeutic targets (andinteractions) compared with biopsies from the enhancing core. Moreimportantly, BAT subpopulations represent the primary targets ofadjuvant therapy and the main contributors to tumor recurrence.Quantification of the diversity and interactions amongmolecularly-distinct subpopulations in the BAT can help improve futuretreatment strategies (such as adaptive therapy), under the paradigm ofindividualized oncology.

Unlike surgical sampling, MRI can help visualize entire tumor volumes,including BAT regions that remain unresected. Further, MRI can measure avariety of complementary biophysical features that reflect regionaltumor phenotypes. On CE-MRI, enhancement reflects regions of disruptedblood brain barrier (BBB), while T2W signal demarcates regions of highwater content and tumoral edema in BAT. Advanced MRI features includetumor cell density on diffusion-weighted imaging (DWI) white matterinfiltration on diffusion tensor imaging (DTI), and microvesselmorphology on Dynamic Susceptibility-weighted contrast-enhancedperfusion MRI (DSCpMRI). In addition, MRI spatially encodes signalintensity values for all voxels contained in each image. The texturalpatterns between voxel intensities and their surrounding neighborsprovide further insight to tissue microstructure and phenotypicheterogeneity within the local environment. These complementary MRIfeatures/phenotypes offer potential biomarkers of underlying genomic andtranscriptomic status, and have been previously correlated withmolecular profiles from GBM. Unfortunately, none of the studies to datehave specifically evaluated intratumoral molecular heterogeneity orquantified the regional diversity of molecularly-distinct tumorsubpopulations.

Multiple molecularly distinct subpopulations can co-exist within the BATof a single GBM tumor, and these subpopulations can exhibit distinctMRI-based signatures depending on the phenotypic expression of theirunderlying genetic aberrations. These MRI-based signatures can beextracted primarily from texture analysis, which improves ML modelaccuracy and correlation with genetic aberrations, compared to using rawMRI signal values alone.

As mentioned above, the systems and methods described in the presentdisclosure use image-recorded stereotactic biopsies to build spatiallyaccurate predictive models of regional molecular diversity andtumor-tumor cooperativity/competition. Spatially matching biopsylocations with co-registered MRI measurements maximizes correlationaccuracy between tissue analysis (e.g., genetic, histologic) andMRI-based signatures.

To this end, the predictive models generated using machine learningalgorithms can be trained on training data that includes magneticresonance imaging (“MRI”) data, histology data, molecular data,biological factor data, or combinations thereof. The histology data andmolecular data are estimated, computed, or otherwise determined fromspatially annotated biopsies that can be spatially co-registered withthe MRI data.

As noted above, each biopsy specimen and sub-specimen can be classifiedbased on molecular data, such as molecular profiles. Molecularsubpopulations can be defined using only CNV data, using both CNV andexome (e.g., mutation) data, or combinations of other molecular data. Asalso described above, the intra-biopsy heterogeneity across the biopsysub-specimens can also be quantified (e.g., over a range of 0 to 1) foreach given molecular marker. In brief, 0 denotes complete absence of themarker across all biopsy sub-specimens, 1 denotes presence of themolecular marker across all biopsy sub-specimens, and an “admixed”category denotes that only some but not all of the sub-specimens expressthe molecular marker.

In general, the machine learning model can be constructed based onmolecular classifications (e.g., 0, 1, admixed) from each biopsyspecimen, sub-specimen, or both, and the corresponding spatially matchedMRI-based features. In other instances, the molecular features,biological features, or both, can be quantified as one or morecontinuous variables rather than as discrete classifications orcategories. The machine learning model can also be constructed based onregional phenotypic niches for each biopsy specimen, sub-specimen, orboth. Phenotypic niches (e.g., hypoxia, angiogenesis, cellproliferation, cell invasion) can be classified based on RNA-sequencingdata and GSEA. Intra-biopsy heterogeneity can be defined across eachsub-specimen as described above.

Referring now to FIG. 1, a flowchart is illustrated as setting forth thesteps of an example method for constructing and implementing a machinelearning model (or algorithm) that is trained to quantify regionalbiological diversity in a region-of-interest in a subject, such as a BATregion. In some instances, the trained machine learning model bygenerate output as one or more images that depict spatial patterns ofone or more biomarkers across the region (e.g., BAT, tumor, or othertissue region). In general, a different machine learning model can betrained for each biomarker. Furthermore, a different machine learningmodel can be trained for different biological factors, such as onemachine learning model for females trained on training data collectedfrom female subjects, and one machine learning model for males trainedon training data collected from male subjects.

Although the method described below with respect to FIG. 1 relates toquantifying the regional diversity of molecular markers (e.g., genomicmarkers, phenotypic markers), it will be appreciated by those skilled inthe art that the systems and methods described in the present disclosurecan also be more generally applied to quantifying biomarkers other thanjust genomic or phenotypic markers. For instance, the regional diversityof any suitable biological feature over a tissue or other anatomicalregion can be quantified by training an appropriate machine learningmodel using imaging data and spatially resolved data that are associatedwith the biological feature. For instance, the biological feature couldinclude physiological, biochemical, or histological information (e.g.,cell density or other histological features such as those described inthe present disclosure) collected from biopsy specimens. As anotherexample, the biomarker may include biological descriptors such as tumoraggressiveness, tumor subtype, or likelihood of recurrence. Thebiomarker may also include or otherwise indicate interactions betweendiverse subpopulations of a tissue region (e.g., tumor, BAT region). Forinstance, the biomarker may include or otherwise indicate cell and/orgeneric interactions between diverse (e.g., molecularly diverse)subpopulations in a tumor or BAT region. Examples of such interactionsare described below with respect to Table 1.

The method includes accessing training data from a memory or other datastorage device or medium, as indicated generally at process block 102.This may include accessing imaging data as indicated at process block104, molecular data as indicated at process block 106, histology data asindicated at process block 108, and/or biological factor data asindicated at process block 110.

As a non-limiting example, imaging data can be collected from subjects(e.g., subjects with stereotactic biopsies) by acquiring images, such aspreoperative multi-parametric magnetic resonance images or other medicalimages (e.g., ultrasound, CT, PET). Thus, in some instances the imagingdata may include MRI data. Spatially annotated stereotactic biopsies arealso collected from across diverse locations, particularly in the BAT.

MRI data can include magnetic resonance images, parametric maps, texturefeature maps, or combinations thereof. Examples of magnetic resonanceimages include T1-weighted images, contrast-enhanced T1-weighted images,T2-weighted images (e.g., T2-weighted fluid attenuation inversionrecovery (“FLAIR”) images), T2*-weighted images, diffusion-weightedimages, susceptibility-weighted images, and so on.

Parametric maps generally include qualitative or quantitative parametermaps generated from magnetic resonance images. Examples of parametricmaps include relative cerebral blood volume (“rCBV”), relative cerebralblood flow (“rCBF”), apparent diffusion coefficient (“ADC”), meandiffusivity (“MD”), fractional anisotropy (“FA”), and so on.

Texture feature maps can include maps depicting texture featuresgenerated from magnetic resonance images, parametric maps, or both. Forinstance, texture features may include first-order statistics orsecond-order statistics computed from magnetic resonance images,parametric maps, or both. As one general example, texture feature mapscan depict texture features computed based on a gray-level co-occurrencematrix (“GLCM”), or may include other texture features known to thoseskilled in the art. Examples of texture features include energy,entropy, contrast, homogeneity, and correlation. Other examples oftexture features may include autocorrelation, cluster prominence,cluster shade, dissimilarity, and so on. Texture features may alsoinclude local binary patterns (“LBP”), discrete orthonormal Stockwelltransform (“DOST”) based texture features, Gabor filter-based texturefeatures, raw mean, and raw standard deviation. In some instances,principal components of the texture feature maps can be computed using aprincipal component analysis (“PCA”) and the principal components can beimplemented as training data to be correlated with molecular status.

As one non-limiting example, the MRI data are multi-parametric data thatinclude six different image types: post-contrast T1-weighted images(“T1+C”), T2-weighted images (“T2W”), relative cerebral blood volume(“rCBV”) on dynamic susceptibility contrast (“DSC”) perfusion,post-contrast T2*-weighted images (“EPI+C”), mean diffusivity (“MD”),and fractional anisotropy (“FA”). These MRI contrasts provide regionalphysiologic metrics of the tumor microenvironment, including vascularleakage and blood-brain barrier (“BBB”) disruption (T1+C); vasogenic andtumoral edema (T2W); microvessel volume and angiogenesis (rCBV); tumorcell density and cell size (EPI+C); cell density and proliferation (MD);and white matter integrity and tumoral infiltration (FA).

As a non-limiting example, regions-of-interest for all co-registeredMRIs measuring 8×8×1 voxels at each biopsy location that roughlycorrespond to the target volume of each tissue specimen (˜250 mg) aregenerated. For image analysis, mean and standard deviation of gray levelintensities and map intensity values within each ROI can be measured andmapped onto the range 0-255. This step helps standardize intensitiesbetween ROIs and reduces effects of intensity non-uniformity on featuresextracted during subsequent texture analysis. Next, texture analysis canbe performed as described above. As one example, three complementaryalgorithms can be used: GLCM, LBP, and DOST. In one example study, 240MRI-texture features and 16 raw features (i.e., mean and SD for 8 MRIcontrasts) were generated from each ROI. The principal components (PCs)for respective MRI-texture algorithms (GLCM, LBP, DOST) for each ROIwere then determined. Each PC will correlate individually with molecularstatus as described below.

For each spatially annotated biopsy, molecular data are also collected.Molecular data can include DNA copy number variants (“CNV”), genemutation (exome), RNA sequencing (“RNA-seq”), gene expression,proteomics analysis data, and so on. These molecular data can bedetermined from the biopsies and spatially matched to the imaging data.

As noted above, the biopsy specimens can be divided into separatesub-specimens to also assess the degree of genetic/molecularheterogeneity within individual biopsy specimens.

Tumoral DNA/RNA analytes can be isolated and multi-level molecularanalysis performed for all biopsy specimens and/or divided biopsysub-specimens. Whole exome sequencing (“WES”) and RNA-seq can beperformed to assess the mutation profile and to quantify transcripts.Somatic coding mutations (e.g., ˜90 Gb, 30× coverage) and geneexpression (e.g., 100 million reads) can also be collected.

As a non-limiting example, molecular markers associated with frequentkey GBM drivers (e.g., EGFR, PDGFRA, PTEN) can be assessed. Additionallyor alternatively, NF1, TP53, CDKN2A, RB!, ATRX, MET, MGMT, and mutationsin the TERT promoter can also be evaluated. RNA sequencing data can beused to validate gene mutation (exome) profiles.

Genomic data (e.g., CNV, exome) can be used to classifymolecularly-distinct subpopulations within each biopsy and RNA-seq canbe used to quantify phenotypic niches (e.g., hypoxia, proliferation,angiogenesis, invasion) for each biopsy. Parameters such as co-existenceand heterogeneity of molecularly-distinct subpopulations and phenotypicniches can be quantified within each biopsy. Combinations ofmolecularly-distinct subpopulations and enriched phenotypic niches inregions of recurrence can also be identified.

As one non-limiting example, molecular data can be estimated, computed,or otherwise determined using one or more of the following analysistechniques or other analysis techniques known to those skilled in theart. Germline and somatic variants can be determined from WES data. Forinstance, WES profiles from a subject's white blood cells for each PDXmodel can serve as germline references for detection of somaticmutations. RNA-seq can be determined using an analysis pipeline thatincludes mapping, read count generation, and junction detection.Differential gene expression analysis can be implemented between rim andcore samples. Integrative analysis of genomic and transcriptomic datacan be performed using a least absolute shrinkage and selection operator(“LASSO”)-type regularization method. Hierarchical/consensus clusteringand principal components analysis (“PCA”) can be used to identifygenomic heterogeneity in the invasive population. Ontology analyses canbe employed to define biological processes, and gene set enrichmentanalysis (“GSEA”) can be used for comparisons of gene signatures in thebiopsy specimens to assess Cancer Hallmark Pathways. Hypoxia,angiogenesis, cell proliferation, and cell invasion gene signatures fromRNA-seq can be curated and used to quantify the phenotypic niches ineach sample using Gene Set Variation Analysis (“GSVA”).

As noted above, additional or alternative molecular analysis techniquescan also be implemented and used to collect molecular data from thespatially annotated biopsies.

In some instances, the molecular data can include a classification oftumor subpopulations based on the molecular analysis of the spatiallyannotated biopsies. For instance, classification of tumor subpopulationscan be performed based on distinct molecular profiles. For each biopsyspecimen, biopsy sub-specimen, or both, the shared commonality ofaberrant CNV and gene mutation of key GBM drivers (e.g., those describedabove) can be used to define molecularly-distinct populations withineach spatially annotated biopsy specimen (e.g., tissue sample) orsub-specimen.

PyClone plots, or other statistical models that can infer the prevalenceof point mutations in heterogeneous tissue samples, can also be used tointegrate molecular data and summarize subpopulation identity. PyCloneis described for example by A. Roth, et al., “PyClone: StatisticalInference of Clonal Population Structure in Cancer,” Nature Methods,2014; 11:396-398.

As one non-limiting example, CNV, which is used to inform the PyClonepriors, can be detected based on a log 2 comparison of normalizedphysical coverage (or clonal coverage) across tumor and normalsequencing data. Somatic mutations and allelic abundance can be calledusing three variant callers: Mutect, Strelka, and Seurat, and thenconsensus variants can be gathered as input to PyClone.

Additionally or alternatively, molecular data can include an estimate oftumor content derived from copy number alteration or from pathologistestimates, which can be supplied to ensure more accurate inference ofclonal population.

For each spatially annotated biopsy, histology data can be collected. Ingeneral, histology data can include qualitative or quantitativehistological features determined, estimated, or computed from or basedon the spatially annotated biopsies. By way of example, the histologicalfeatures can generally include quantitative information derived fromhistological samples. For example, the histological features can beassociated with the size, shape, area, or number of cells, includingseparate measurements for different cell types.

As one non-limiting example, the histological feature can be celldensity, which is a measurement of the number of cells in a given volumeof tissue. As another example, the histological feature can bevoxel-wise percentage of necrosis or vascularity.

As another non-limiting example, histological features can includequantitative information derived from staining. For instance, thehistological features can be associated with the presence of differentcolors in a sample, the area of a given color in a sample, the relativeproportions of different colors in a sample, or the intensity of one ormore colors in a sample.

In some instances, the stain can an immunohistochemical stain withcell-type specific markers for stromal cell types (e.g., reactiveastrocytes, activated microglia). In these instances, the histologicalfeatures can include quantitative information about the cellular (e.g.,stromal) milieu.

When the stain is a fluorescent molecule, the histological feature mayinclude quantifiable information derived from fluorescent imaging of thehistological sample, such as fluorescence intensity. As an example, thehistological feature can be any characteristic that can be quantified orlabeled from histology using a microscope or other tool for evaluatingtissue slides.

One example of how histological features can be obtained is nowdescribed. Tissue samples that have been previously imaged can bestained. As one example, the samples can be stained using an H&E stain,an immunohistochemical stain, or so on. Each tissue slide can then bephotographed across the entire sample. For instance, a motorizedmicroscope stage can be used to obtain photographs of the stainedslides. In some instances, each photograph can be processed individuallyand stitched together to form a histology image of the tissue sample.

Additional processing can be performed on the histology image asnecessary or desired. For example, to obtain a cell density measurement,thresholds for segmentation of cell nuclei can be applied and, followingsegmentation, the nuclei in each histology image can be counted toobtain a voxel-wise measurement of cell density. In some otherembodiments, a clustering algorithm can be applied to segment thehistology image. For instance, a k-means clustering algorithm can beapplied to each histology image for segmentation of cell nuclei andanatomical features.

By way of example, spatially annotated biopsies can be provided andsegmented into histology tissue types, histological features, or both,and then co-registered to the MRI data.

In some instances, the spatially annotated biopsies can be divided intoseparate sub-specimens to assess the degree of histological featurehomogeneity or heterogeneity within individual biopsy specimens. As oneexample, the spatially annotated biopsies can be divided into quarters.

Biological factor data may include information such as the subject's sex(e.g., male, female), age, and so on. It will be appreciated by thoseskilled in the art that the training data may also include morphologicaldata or any other qualitative or quantitative descriptor of the biopsyspecimens or sub-specimens.

Referring still to FIG. 1, using the molecular data, regional moleculardiversity in the plurality of subjects from which the training data havebeen collected is quantified, as indicated at step 112. For instance,based on the molecular data, the co-existence and heterogeneity ofmolecularly-distinct subpopulations within each biopsy can bequantified. In some instances, this can include quantifyingintra-specimen heterogeneity. Genomic identity can be determined (e.g.,based on CNV and exome) for each biopsy sub-specimen to assess thedegree of consistency/agreement across all sub-specimens. Although thismay not represent single cell level heterogeneity, these data canprovide insight into the heterogeneity observed at the scale of asub-specimen (e.g., 250 mg in some instances) biopsy. This provides theadditional benefit of increasing sensitivity of detecting specificmolecular markers within the biopsy specimen.

For any given molecular marker, the heterogeneity of that single markercan be represented from 0 to 1 to denote being present in none, some, orall of the sub-specimens (e.g., none, one-quarter, one-half,three-quarters, or all four segments when biopsy is divided intoquarters). To further quantify the heterogeneity within each biopsy aShannon index,

${H = {- {\sum\limits_{i = 1}^{N}\; {p_{i}\mspace{14mu} \ln \mspace{14mu} ( p_{i} )}}}},$

where in this case p_(i) is the proportion of the biopsy belonging to amolecularly distinct subpopulation, i, and there are N totalsubpopulations. Analysis with PyClone can provide the proportions,p_(i), for the various subpopulations.

After the Shannon index is computed for each biopsy, these values can becompared across multiple biopsies within each subject. The Shannon indexvalues can also be compared across different subjects using descriptivestatistics. Possible correlations with spatial locations on the MRI canbe examined (e.g., is there a predominance for higher diversity incertain BAT regions).

In some instances, quantifying the co-existence and heterogeneity ofmolecularly-distinct subpopulations within each biopsy can includequantifying correlations between markers. As an example, for eachsubject, and for each pair of molecular markers, a chi-square test canbe used quantify the significance of any spatial association between themarkers. The sign of the associated correlation coefficient can besuggestive of cooperative (+) or competitive (−) dynamics of thesubpopulations.

Quantifying the co-existence and heterogeneity of molecularly-distinctsubpopulations within each biopsy can also include identifyingcombinations of molecularly-distinct subpopulations and enrichedphenotypic niches in regions of recurrence. In these instances, subjectsfrom whom data are collected will undergo therapy, such as standardadjuvant therapy (e.g., Stupp protocol). Serial multi-parametric MRIdata are acquired from these subjects during and after the adjuvanttherapy to evaluate for MRI-enhancing lesions that indicate tumorrecurrence. Upon development of enhancing recurrent lesions on MRI ineach subject, the regions-of-interest (“ROIs”) will be segmented toencompass the recurrent lesions. The surveillance MRI data (showing therecurrence) can then be coregistered with the initial surgical MRI data(used to spatially annotate biopsy locations in the primary GBM tumor).The images can then be overlaid to determine which biopsy specimens (andcorresponding molecular profiles and MGMT methylation status) spatiallyco-localize with the region of recurrence. Further, the time toprogression (“TTP”) for each region of recurrence, which gives anindication of the recurrent tumor growth rate, can be determined. TTPcan be quantified as the amount of time needed to manifest tumorrecurrence within a specific region of the BAT. As such, the incidenceand rate of recurrent tumor growth for molecularly-distinctsubpopulations and associated niches as defined by the molecularprofiles can be quantified within each biopsy specimen.

In some instances, these subjects can undergo surgicalre-resection/re-biopsy of recurrent lesions. In these cases,pre-operative multiparametric MRI and spatially annotated stereotacticbiopsies can be further acquired from the regions of tumor recurrence.As described above, the tissue samples may also be divided in intosub-specimens. Molecular analyses can then be performed to collectadditional molecular data for these subjects in the recurrent regions.

To confirm that MRI enhancement represents tumor recurrence (as opposedto post-treatment effect), the presence of tumor recurrence can beevaluated using a perfusion MRI Fractional Tumor Burden (“FTB”) method,as described by L. Hu, et al., in “Reevaluating the Imaging Definitionof Tumor Progression: Perfusion MRI Quantifies Recurrent GlioblastomaTumor Fraction, Pseudoprogression, and Radiation Necrosis to PredictSurvival,” Neuro. Oncol., 2012; 14:919-930, or using other evaluativemethods.

As described above, quantifying regional molecular diversity can alsoinclude building a three-class classifier based on the 0, 1, and admixedclasses that denote the absence, presence, or admixture of molecularmarkers across biopsy sub-specimens. Exome data can be incorporated torefine the quantification of the heterogeneity status for each biopsysample.

Referring still to FIG. 1, a machine learning model is then trainedusing the training data and the quantified molecular diversity, asindicated at step 114. The machine learning algorithms used can includemachine learning algorithms known to those skilled in the art. Forinstance, a Principal Component Analysis (“PCA”) can be used fordimension reduction of texture features. Machine learning algorithmsthat can be used to build classifiers between MRI features and molecularmarkers can include Support Vector Machines (“SVM”), Linear DiscriminantAnalysis (“LDA”), Quadratic Discriminant Analysis (“QDA”), Decision Tree(“DT”), and so on. In some instances, the machine learning model mayimplement an active learning model or a transfer learning model.

As noted above, a different machine learning model can be trained foreach combination of molecular marker and biological factor. Forinstance, a different machine learning model can be trained for eachmolecular marker of interest for a first biological factor, and thenanother machine learning model can be trained for each molecular markerof interest for a second biological factor. In this way, biologicalfactor-specific machine learning models can be trained for each desiredmolecular marker. For instance, female-specific and male-specificmachine learning models can be trained.

Molecular markers can refer to genomic markers (e.g., EGFR, PDGFRA,PTEN, NF1, TP53, CDKN2A, RB1, ATRX, or MET), phenotypic markers (e.g.,hypoxia, angiogenesis, cell proliferation, cell invasion), or both.

The trained machine learning models are implemented to generate one ormore images that depict regional molecular diversity by applying MRIdata acquired from a new subject to the one or more trained machinelearning models, as indicated at step 116. For instance,multi-parametric MRI data can be input to the different trained machinelearning models to generate separate maps for the molecularly-definedclassifications defined above. These maps will display probabilities foreach voxel regarding whether there is high or low confidence that thevoxel corresponds to the presence of the particular molecularsubpopulation of interest. This process can be repeated to generate mapsfor a panel of molecularly-distinct classifications. The molecularclassification MRI-based ML maps can then be combined (e.g., byoverlaying on each other) in order to generate a single image thatdepicts co-existing molecularly distinct subpopulations and/orphenotypic niches.

As noted above, in some examples the trained machine learning models mayimplement a transfer learning model. In these instances, imaging dataand initial tissue data (e.g., biopsy data) collected from the subjectcan be input to the trained machine learning models. Such models can beused to predict spatial patterns of biomarkers (e.g., molecular markers)throughout a tissue region (e.g., a tumor, a BAT region, other braintissue regions) for subsequent biopsies as well as regions that have notbeen or will not be biopsied in the subject. These embodiments can alsobe facilitated by adding the ability to input biological/molecular dataas a continuous variable (e.g., not just categorical of 0, 1, admixed).Examples of transfer learning models that can be implemented aredescribed, for example, in co-pending Patent Application No.PCT/US2018/061887, which is herein incorporated by reference in itsentirety.

Thus, using the systems and methods described in the present disclosureone machine learning model can be trained for each molecular marker(e.g., genomic/phenotypic marker) using MRI data. As noted, machinelearning models can be separately trained for different biologicalfactors, such as sex or age. As an example, a machine learningclassifier can be trained using texture features of an ROI at eachbiopsy location and the heterogeneity status of the molecular marker(e.g., the genomic/phenotypic marker) of the biopsy. Machine learningalgorithms (e.g., SVM, LDA, QDA, DT) can be used to build thisclassifier.

These trained machine learning models can then be used to generateprobabilistic maps for molecular marker (e.g., genomic/phenotypicmarker) status across the BAT. Furthermore, a classifier can be used togenerate a prediction for each window sliding over the entire BAT (withone pixel offset between successive windows) using the texture featurescomputed for that window. As an example, the window can be an 8×8window. In this way, a predictive map for the BAT can be generated. Apredictive map can be generated for each genomic and phenotypic markerof interest. As noted above, the prediction can be a probabilistic scorequantifying the certainty/uncertainty for marker existence within eachlocalized region across the entire tumor, including the BAT zone. Inthis way, the spatial heterogeneity of genomic subpopulations andphenotypic niches within BAT can be quantified.

An example workflow of constructing and implementing a machine learningmodel according to some embodiments described in the present disclosureis shown in FIG. 2.

As described above, the systems and methods described in the presentdisclosure are advantageous for translational imaging. For instance,regional recurrence on serial surveillance MRI can be defined andcoregistered images with initial surgical multi-parametric MRI data.This enables ML predictive maps of molecularly distinct subpopulationsand regional phenotypes to be overlaid to quantify combinations thatco-localize to regional recurrence. Regional variations in TTP thatindicate recurrent tumor growth rates associated with localmolecularly-distinct subpopulations and co-localized phenotypic nichescan also be determined.

It is contemplated that BAT will demonstrate specific patterns ofsubsequent recurrence on serial surveillance multi-parametric MRI. TheTTP, or the amount of time needed to manifest tumor recurrence within aspecific region of the BAT, can be quantified to further guide suchanalyses.

Using the systems and methods described in the present disclosure,MRI-based maps of molecularly-distinct subpopulations (based on thesurgical MRI) can be co-localized within the corresponding spatiallocations of regional tumor recurrence. By further overlaying MRI-basedML maps of regional phenotype, combinations of molecularly-distinctsubpopulations and phenotypic niches that enrich for regional recurrencecan be identified. The incidence and rate of recurrent tumor growth forthe molecularly-distinct subpopulations and associated phenotypic nichescan also be quantified. As another example use, in a subset of recurrentGBM patients undergoing stereotactic biopsy and measurement of spatiallymatched MRI metrics, spatially matched primary and recurrent genomicsubpopulations can be matched to model local evolution ofmolecularly-distinct subpopulations.

Using the systems and methods described in the present disclosuremolecularly-distinct subpopulation and phenotypic niche interactionspredicted by clinical GBM data can be evaluated using PDX models invitro and in vivo with appropriate molecularly defined subpopulations.PDX models can be used to quantify and assess the competition andcooperative dynamics of tumor subpopulations with genetic markers ofinterest.

Intracranial implantation of the PDX in mice gives rise to tumorstypical of patient GBM including nuclear atypia, vascular involvement intumor core, and significant invasion at the tumor/normal braininterface. These models can be used to quantify thecooperative/competitive dynamics among molecularly-distinctsubpopulations. For these analyses, relevant biological variables can beseparately accounted for. For instance, biological variables such as ageand sex of the patients contributing the PDX cell lines can be recorded.

As one example, in vitro data may include a panel of primary GBMxenograft cell lines with the appropriate genetic markers based on thoseidentified in human GBM tumors. This may involve key GBM driveralterations, such as EGFR and PDGFRA amp. For example, GBM8 (EGFRwildtype amp) and GBM85 (PDGFRAamp) can be utilized to quantify theco-existence of these molecularly-distinct subpopulations. As newsubpopulations are identified and characterized using the systems andmethods described in the present disclosure, appropriate PDX cell modelscan be chosen to test the cooperative and competitive natures.

As one example study design, to enable co-expression of two reportergenes in each xenograft cell line, a 2A peptide enabled lentiviralconstruct encoding Firefly Luciferase (FLuc) and RFP (FLuc-T2A-RFP;Biosettia) or a lentiviral construct encoding Renilla Luciferase andgreen fluorescent protein (RLuc-T2A-GFP; GenTarget Inc.) can be used.GBM8 can be infected with lentiviruses expressing FLuc-T2A-RFP, andGBM85 can be infected with lentiviruses expressing RLuc-GFP. Positivelytransduced GBM8 and GBM85 cells can be enriched (>98%) by FACS sorting,expanded by growth as orthotopic flank tumors in mice, and cryopreservedfor use in proposed studies. Cell growth and motility can be performedon the molecularly defined PDX cells alone and in co-culture conditionsto assess competition and cooperation. For cell growth assays,CellTiterGlo (Promega) can be used. Secondary assays in 2D can include amultiplexed BrdU (DNA synthesis) and Ki-67 (resting/cycling) assay,which more specifically quantifies cell proliferation. Furtherrefinement, as needed, can be carried out using assay conditions withlimiting growth factors to assess competition or synergy of theco-cultured subpopulations. In parallel, the in vitro bioluminescencefor each cell line alone and in combination can be assessed to determinethe correlation between cell number and the bioluminescence signal.Cells can be seeded at different concentrations and substrates for eachluciferase (1 μg/ml coelenterazine for Rluc and 1.5 μg/ml D-luciferinfor Fluc) can be added to the medium. Luciferase activity can bemeasured using a microplate reader at the appropriate wavelength. Forcell motility assay (“Migration/Invasion”), cell migration can beassessed using the uncoated Transwell Boyden Chamber assay, whereas cellinvasion can be assessed in the Boyden Chamber Matrigel invasion assay,wherein laminin-rich matrix serves as a good surrogate for brain stroma.Cells migrating through the chamber can be quantified by blindedobservers.

As another example study design, primary GBM xenograft cell lines withthe appropriate genetic markers based on those identified in the humanGBM tumors can be selected. For instance, with EGFR and PDGFRAamplifications, intracranial xenografts can be established in nude mice.Mice can be randomized to groups of 10 and can receive GBM8-FLuc-T2A-RFPor GBM85-RLuc-GFP neurosphere cells. Cells (300,000) can bestereotactically implanted into right basal ganglia. For co-cultureexperiments, equal cell numbers of GBM8 and GBM85 can be mixed and thecell mixture implanted. At 2-3 weeks post-implant, two animals from eachgroup can be sacrificed to verify equivalent tumor growth in all groups.For the rest of the animals, mice can be imaged for Rluc activity byinjecting coelenterazine (100 μg/animal in 150 μl of saline)intravenously via the tail vein and 5 min later, photon counts can berecorded over 5 min using the IVIS imaging system. To image Fluc, micecan be given intraperitoneal injection of D-luciferin (4.5 mg/animal in150 μl of saline), and photon counts can be recorded 5 min afterD-luciferin administration over 5 min.

The total photon flux (photons/sec) can be measured from a region ofinterest over the skull using the Living Imaging software. Mice can beimaged every 3 days until reaching a moribund state. Tumor bearingbrains can be harvested for serial sectioning and immunohistochemicalanalysis.

Ex vivo fluorescence imaging on cryosections of tumor bearing brain canbe performed. Immediately after sacrificing the animals, the wholebrains can be dissected and embedded into O.C.T. Series of coronalsections (15 μm) can be cut from the O.C.T. embedded brain and tumortissues can be identified by crystal violet staining. The adjacenttissue sections can be used for fluorescence microscopic study. The exvivo images can be taken by using the living Imaging software as usedfor the in vivo studies above. Quantification of RFP and GFP cells canbe obtained through the common region of interest on both the tumor sideand the contralateral normal brain of each image. A total of photoncount (counts/s) in the identical region of interest can be used forcomparison of dynamic change in signal intensity.

Histology and fluorescence microscopy can also be performed. Brains canbe removed from euthanized mice and histological (H&E staining) andimmunohistochemical (IHC) analyses performed to assess cellproliferation (Ki-67), cell death (activated caspase-3), andquantification of the population of RFP or GFP cells using fluorescencemicroscopy. Scoring of IHC can be performed in a semi-quantitativemanner. Due to variable number of tumor cells in each of the tissuesections, the percent of tumor cells that stain can be scored as 1(<5%), 2 (5 to 50%), and 3 (>50%). Stain intensity can be scored as 1(low), 2 (moderate), or 3 (high). The total IHC score can be the productof the percent cells stained and the stain intensity thereby giving asemiquantitative range of 1 to 9. A product score >4 can be designatedas significant. Analysis of glioma cell spread can be scored from theH&E stained sections on a scale of I-IV according to the followingcriteria: I—unilateral and well demarcated, II—fuzzy border and/orshowing movement into other hemisphere, IIIA—bilateral, but restrictedto around midline structures, IIIB—bilateral with clear involvement ofboth hemispheres, IV—largely replacing the brain.

To investigate the therapy response of PDX to standard first-linetherapy of concurrent temozolomide (TMZ) and radiation therapy (RT)followed by adjuvant TMZ can be performed. Briefly, mice withestablished GBM8 FLuc-RFP, GBM85 RLuc-GFP, or GBM8 FLuc-RFP+GBM85RLuc-GFP intracranial tumors as assessed by IVIS imaging can berandomized to therapy groups of (1) placebo+RT, (2) TMZ (66 mg/kg dailyfor 5 days) alone, (3) RT alone (2 Gy twice a day for 5 days), or (4)concomitant TMZ and RT. Mice can be imaged by IVIS twice weekly untilreaching a moribund state. Following sacrifice, brains can be resected,embedded, and sectioned for IHC analysis of tumor cell dispersion, cellproliferation, and apoptosis as described above. Correlativefluorescence microscopy of the same sections will positively identifyimplanted xenograft cells and to determine the relative proportion ofPDX in the mixed tumor. Comparison of xenograft survival when implantedalone relative to implantation as a mixed tumor can be analyzed todetermine cooperativity in treatment resistance.

Using data collected from in vitro studies, such as those mentionedabove, dynamic models for competition/cooperation can be developed. Forexample, a Lotka-Volterra model describing the interaction between twocell populations can be parameterized. To illustrate this example,PDGFRAamp and EGFRamp can be considered:

$\begin{matrix}{{\frac{{dN}_{PDGFRA}}{dt} = {\frac{r_{PDGFRA}}{K_{PDGFRA}}{N_{PDGFRA}( {K_{PDGFRA} - N_{PDGFRA} + {\alpha_{PE}N_{PDGFRA}}} )}}};} & (1) \\{\mspace{76mu} {{\frac{{dN}_{EGFR}}{dt} = {\frac{r_{EGFR}}{K_{EGFR}}{N_{EGFR}( {K_{EGFR} - N_{EGFR} + {\alpha_{EP}N_{EGFR}}} )}}};}} & (2)\end{matrix}$

where N_(PDGFRA) and N_(EGFR) are the numbers of PDGFRAamp and EGFRampcells, respectively, which can be determined from cell counts fromCellTiterGlo or using other methods; r_(PDGFRA) and r_(EGFR) are growthrates for PDGFRAamp and EGFRamp cells, respectively, which can bedetermined from BrdU labeling data from in vivo cell cultures;K_(PDGFRA) and K_(EGFR) are carrying capacity for PDGFRAamp and EGFRampcells, respectively, which can be determined from cell counts in singlecell cultures; and α_(PE) and α_(EP) correspond to the effects ofPDGFRAamp cells upon EGFRamp cells, and that of EGFRamp cells uponPDGFRAamp cells, respectively, which can be estimated by fitting themodel to cell count curves with a nonlinear regression.

Cell counts at multiple time points obtained from in vitro studies cangenerate experimentally-derived growth curves. Fitting this model tothese curves (e.g., with a mixed effects nonlinear regression) can bedone to determine the corresponding a parameter values. These willcharacterize the nature of the relationship between the populations asshown in Table 1 below, with the numeric values of the a's indicatingthe relative degree of the effects.

TABLE 1 Signs of the α derived from fitting the model to experimentaldata describe the nature of cell population interactions. α_(PE) α_(EP)Type of Interaction 0 0 neutralism: no negative or positive effects − 0amensalism: P negatively affected by E 0 − amensalism: E negativelyaffected by P + 0 commensalism: P positively affected by E 0 +commensalism: E positively affected by P − − competition + +mutualism/cooperation + − parasitism: of P upon E − + parasitism: of Eupon P P = PDGFRAamp cells, and E = EGFRamp cells. (+) indicatesbenefit, (−) indicates cost, and (0) indicates no effect.

This process can be repeated with data from the PDX models describedabove to characterize the relationship between cells in an in vivosetting, the interactions of cell populations in different environmentsto be compared and contrasted. This can be achieved by obtaining cellestimates from bioluminescence images, using a conversion factor torelate signal intensity to cell number.

Using the MRI-based models developed using the systems and methodsdescribed in the present disclosure, the abundance ofmolecularly-distinct subpopulations within each tumor can be quantifiedusing multiple serial MRI examinations from the time of primary surgeryto the time of tumor recurrence. Specifically, the segmented predictedvolume of each molecular subpopulation can be quantified and relativevolumetric changes at each imaging time point can be plotted. Trackingthe abundance of distinct subpopulations over time can help infer tumorbehavior and the cooperative and competitive interactions betweensubpopulations over the course of therapy.

These dynamics can be validated in a subset of patients undergoingstereotactic biopsy and molecular profiling of recurrent tumor samplesto confirm the relative abundance of molecularly-distinct subpopulationscorresponding to regional recurrence. In particular, regions ofrecurrence that are spatially matched to previously biopsied BAT regionscan be targeted at the time of primary surgery. In the event that GBMtreatment impacts the MRI-based signatures of primary (untreated) GBM,these recurrent biopsy samples and spatially matched MRI measurementscan be further used to build MRI predictive models specifically forrecurrent tumor in the post-treatment setting, as mentioned above.

FIG. 3 shows an example where regional molecular combinations exhibitdifferent recurrent patterns in a single GBM patient. Two separatebiopsies (Bx #1, Bx #2) were genomically profiled for CMV status in a 57y/o GBM patient who was subsequently treated with chemo-radiotherapy. Bx#1 (top row, red circle) showed a tumor population with only EGFRamplification (amp). This region did not recur until 27 months afterbaseline. Meanwhile, Bx #2 (bottom row, green circle) exhibited bothEGFRamp and PDGFRamp, which likely represented two molecularly-distinctsubpopulations. This region recurred earlier with a time to progression(TTP) of 19 months. These preliminary data suggest that interactionsbetween combinations of molecularly-distinct subpopulations can impacttumor behavior and likelihood of recurrence over time. Therefore, insome embodiments the systems and methods described in the presentdisclosure can be implemented to generate a report that indicates alikelihood of recurrence over time. In some instances, such a report andinclude an estimated TTP, which may be estimated based on one or moremolecular marker maps generated using one or more trained machinelearning models. Additionally or alternatively, such a report mayinclude one or more molecular marker maps generated using one or moretrained machine learning models, which can be displayed to a user. Insome instances, these maps can be combined into a single map thatquantifies or otherwise depicts regional molecular diversity in aregion, such as a BAT region, a tumor region, or both. For instance,EGFR and PDGFRA amplified maps at the biopsy regions are depicted inFIG. 3 as being overlaid on the MRI images to illustrate the use ofthese modeling maps to predict biological activity/response and/ortreatment outcome.

FIGS. 4A and 4B show an example of spatial anti-correlation betweenmodel-predicted EGFR amplified (amp) and PDGFR amp subpopulations. FIG.4A is a scatter plot of model-predicted probability of EGFR amp (x-axis)versus PDGFRA amp (y-axis) for all tumor voxels across 13 GBM patients.The shaded boxes show >80% probability of either EGFR amp (verticallyoriented box, shaded red) or PDGFRA amp (horizontally oriented box,shaded green). Only 8.2% of the regions in this example were predictedto contain both amplifications (where the two shaded boxes overlap)(Fisher's exact, p=0.0037). FIG. 4B shows separate ML-generatedmolecular marker maps for EGFR amp (left) and PDGFRA amp (right)overlaid on the same MRI image slice with probability color bars (0-1)showing colored regions as the highest probability of amplification. Thegreen (upper) circles demarcate an anterior regional subpopulation withPDGFRA amp but normal EGFR, while the red (lower) circles show aposterior subpopulation with EGFR amp but normal PDGFRA. These dataillustrate how MRI-based predictive maps can be used to co-localizeneighboring molecularly-distinct subpopulations to infer cooperative orcompetitive dynamics.

FIG. 5 shows an example outline of a methodology for addressingmulti-scale spatial heterogeneity and temporal dynamics using thesystems and methods described in the present disclosure. As describedabove, image-recorded stereotactic biopsies can be collected and basedon these the intra-biopsy heterogeneity of molecular markers of interestcan be quantified. These biopsies can be integrated with spatiallymatched MRI signatures to inform machine learning models that predictspatial patterns of molecular markers across all subregions for theentire tumor. Potential cooperation and competition dynamics can also beidentified from co-incidence analysis using biopsies and ML-basedmolecular marker maps. As noted above, predicted temporal dynamics ofcooperation and competition can be quantified and validated in PDX modelcell lines expressing appropriate molecular markers of interest. In thisway, the clinical relevance of these model-predicted dynamics betweenmolecularly-distinct subpopulations using ML models and serial MRI inhuman GBM can be further validated.

FIG. 6 shows an example of regional differences in response to EGFRinhibition therapy. These results correlate with predictive maps ofwhether a tumor region is EGFR amplified (i.e., sensitive to EGFRinhibitor therapy) or non-amplified (i.e., insensitive to therapy. Inthis way, the biomarker maps generated using the systems and methodsdescribed in the present disclosure can also provide information aboutthe efficacy or other evaluative information of a particular treatment.

Although the systems and methods have been described with respect toquantifying the regional diversity of molecular markers in a tissueregion (e.g., tumor, BAT), it will be appreciated by those skilled inthe art that the systems and methods can also be adapted for moregeneral use in any spatially resolved system in which image localizeddata are available or otherwise possible. For instance, the machinelearning models can be constructed based on training data that includespatially sampled data for which a corresponding 2D or 3D image exists,such that the spatially sampled data can be associated with locationswithin the image. As a non-limiting example, the image could be asatellite image of a geographic region and the spatially sample data mayinclude a local readout of a system or population for which it isdesired to model the dynamics over space and time, potentially asinteracting with other similarly defined populations or systems.

As one example, the systems and methods described in the presentdisclosure could be adapted for use in quantifying regional diversity,or otherwise modeling the dynamics of, health data, epidemiologicaldata, or other spatially resolved data over a spatial region, which insome non-limiting examples may be a geographical region. In theseinstances, machine learning models would be trained on training datathat include spatial data or geographic data (e.g., satellite images,census data) rather than MRI data and associated spatially resolved datarather than molecular data collected from spatially annotated biopsyspecimens.

For instance, the MRI data described above could be replaced with othertypes of spatial data, and the molecular data could be replaced withlocalized data that is distributed and associated with a spatial region.Therefore, like the molecular data collected from spatially annotatedbiopsy specimens can be correlated with multiparametric MRI data, so canmore general spatially localized data be correlated with spatial data.In this way, machine learning models can be trained to generate mapsthat quantify the regional diversity of spatially localized data acrossa spatial region, which in some non-limiting examples may be ageographical region. These maps can, in turn, be used to model orotherwise evaluate the spatial and/or temporal dynamics of the localizeddata within that spatial or geographical region.

As one example, the localized data can include health record data forpatients living in a certain geographical region. The health record datacan be associated, or otherwise spatially localized, with locations inthe geographical region.

As another example, the localized data can include epidemiological datafor a certain geographical region. The epidemiological data areassociated, or otherwise spatially localized, with locations in thegeographical region. For instance, the epidemiological data may includelocal readouts or other data about the abundance of one or moreparticular disease epidemics in a geographical region.

As another example, the localized data can include histological data fora certain organ region. The histological data are associated, orotherwise spatially localized, with locations in the organ region. Forinstance, the histological data may include quantification about theabundance of cells or cell markers in an organ region.

As still another example, the localized data can include demographicdata (e.g., gender, age, ethnicity, socioeconomic data), ecologicaldata, geological data, geophysical data, or other spatially resolveddata that may be associated with a certain geographical region.

The geographical data can generally include data that defines or depictsgeographical regions. For instance, the geographical data may includesatellite images of the geographical region. As another example, thegeographical data may include census data that defines geographicalregions (e.g., census tracts, census block groups, census blocks).

Referring now to FIG. 7, an example of a system 700 for generating mapsthat quantifying regional diversity of spatially resolved data in one ormore regions, in accordance with some embodiments of the systems andmethods described in the present disclosure is shown. As shown in FIG.7, a computing device 750 can receive one or more types of data (e.g.,MRI data or other imaging data, molecular data, histology data,biological factor data, geographic data, localized data, spatiallyresolved data) from data source 702, which may be an MRI data source, amolecular data source, a histology data source, a biological factor datasource, a localized data source, a spatially resolved data source, orcombinations thereof. In some embodiments, computing device 750 canexecute at least a portion of a spatially resolved data map generatingsystem 704 to construct and implement machine learning models that areable to generate maps that depict spatial patterns in spatially resolveddata (e.g., molecular markers, biomarkers) from data received from thedata source 702.

Additionally or alternatively, in some embodiments, the computing device750 can communicate information about data received from the data source702 to a server 752 over a communication network 754, which can executeat least a portion of the spatially resolved data map generating system704 to construct and implement machine learning models that are able togenerate maps that depict spatial patterns in spatially resolved data(e.g., molecular markers, biomarkers) from data received from the datasource 702. In such embodiments, the server 752 can return informationto the computing device 750 (and/or any other suitable computing device)indicative of an output of the spatially resolved data map generatingsystem 704.

In some embodiments, computing device 750 and/or server 752 can be anysuitable computing device or combination of devices, such as a desktopcomputer, a laptop computer, a smartphone, a tablet computer, a wearablecomputer, a server computer, a virtual machine being executed by aphysical computing device, and so on. The computing device 750 and/orserver 752 can also reconstruct images from the data.

In some embodiments, data source 702 can be any suitable source ofimaging data (e.g., measurement data, images reconstructed frommeasurement data), such as an MRI system or other medical or opticalimaging system, another computing device (e.g., a server storing imagedata), and so on. In some embodiments, data source 702 can be local tocomputing device 750. For example, data source 702 can be incorporatedwith computing device 750 (e.g., computing device 750 can be configuredas part of a device for capturing, scanning, and/or storing images). Asanother example, data source 702 can be connected to computing device750 by a cable, a direct wireless link, and so on. Additionally oralternatively, in some embodiments, data source 702 can be locatedlocally and/or remotely from computing device 750, and can communicatedata to computing device 750 (and/or server 752) via a communicationnetwork (e.g., communication network 754).

In some embodiments, communication network 754 can be any suitablecommunication network or combination of communication networks. Forexample, communication network 754 can include a Wi-Fi network (whichcan include one or more wireless routers, one or more switches, etc.), apeer-to-peer network (e.g., a Bluetooth network), a cellular network(e.g., a 3G network, a 4G network, etc., complying with any suitablestandard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), a wirednetwork, and so on. In some embodiments, communication network 108 canbe a local area network, a wide area network, a public network (e.g.,the Internet), a private or semi-private network (e.g., a corporate oruniversity intranet), any other suitable type of network, or anysuitable combination of networks. Communications links shown in FIG. 7can each be any suitable communications link or combination ofcommunications links, such as wired links, fiber optic links, Wi-Filinks, Bluetooth links, cellular links, and so on.

Referring now to FIG. 8, an example of hardware 800 that can be used toimplement data source 702, computing device 750, and server 754 inaccordance with some embodiments of the systems and methods described inthe present disclosure is shown. As shown in FIG. 8, in someembodiments, computing device 750 can include a processor 802, a display804, one or more inputs 806, one or more communication systems 808,and/or memory 810. In some embodiments, processor 802 can be anysuitable hardware processor or combination of processors, such as acentral processing unit (“CPU”), a graphics processing unit (“GPU”), andso on. In some embodiments, display 804 can include any suitable displaydevices, such as a computer monitor, a touchscreen, a television, and soon. In some embodiments, inputs 806 can include any suitable inputdevices and/or sensors that can be used to receive user input, such as akeyboard, a mouse, a touchscreen, a microphone, and so on.

In some embodiments, communications systems 808 can include any suitablehardware, firmware, and/or software for communicating information overcommunication network 754 and/or any other suitable communicationnetworks. For example, communications systems 808 can include one ormore transceivers, one or more communication chips and/or chip sets, andso on. In a more particular example, communications systems 808 caninclude hardware, firmware and/or software that can be used to establisha Wi-Fi connection, a Bluetooth connection, a cellular connection, anEthernet connection, and so on.

In some embodiments, memory 810 can include any suitable storage deviceor devices that can be used to store instructions, values, data, or thelike, that can be used, for example, by processor 802 to present contentusing display 804, to communicate with server 752 via communicationssystem(s) 808, and so on. Memory 810 can include any suitable volatilememory, non-volatile memory, storage, or any suitable combinationthereof. For example, memory 810 can include RAM, ROM, EEPROM, one ormore flash drives, one or more hard disks, one or more solid statedrives, one or more optical drives, and so on. In some embodiments,memory 810 can have encoded thereon, or otherwise stored therein, acomputer program for controlling operation of computing device 750. Insuch embodiments, processor 802 can execute at least a portion of thecomputer program to present content (e.g., images, user interfaces,graphics, tables), receive content from server 752, transmit informationto server 752, and so on.

In some embodiments, server 752 can include a processor 812, a display814, one or more inputs 816, one or more communications systems 818,and/or memory 820. In some embodiments, processor 812 can be anysuitable hardware processor or combination of processors, such as a CPU,a GPU, and so on. In some embodiments, display 814 can include anysuitable display devices, such as a computer monitor, a touchscreen, atelevision, and so on. In some embodiments, inputs 816 can include anysuitable input devices and/or sensors that can be used to receive userinput, such as a keyboard, a mouse, a touchscreen, a microphone, and soon.

In some embodiments, communications systems 818 can include any suitablehardware, firmware, and/or software for communicating information overcommunication network 754 and/or any other suitable communicationnetworks. For example, communications systems 818 can include one ormore transceivers, one or more communication chips and/or chip sets, andso on. In a more particular example, communications systems 818 caninclude hardware, firmware and/or software that can be used to establisha Wi-Fi connection, a Bluetooth connection, a cellular connection, anEthernet connection, and so on.

In some embodiments, memory 820 can include any suitable storage deviceor devices that can be used to store instructions, values, data, or thelike, that can be used, for example, by processor 812 to present contentusing display 814, to communicate with one or more computing devices750, and so on. Memory 820 can include any suitable volatile memory,non-volatile memory, storage, or any suitable combination thereof. Forexample, memory 820 can include RAM, ROM, EEPROM, one or more flashdrives, one or more hard disks, one or more solid state drives, one ormore optical drives, and so on. In some embodiments, memory 820 can haveencoded thereon a server program for controlling operation of server752. In such embodiments, processor 812 can execute at least a portionof the server program to transmit information and/or content (e.g.,data, images, a user interface) to one or more computing devices 750,receive information and/or content from one or more computing devices750, receive instructions from one or more devices (e.g., a personalcomputer, a laptop computer, a tablet computer, a smartphone), and soon.

In some embodiments, data source 702 can include a processor 822, one ormore data acquisition systems 824, one or more communications systems826, and/or memory 828. In some embodiments, processor 822 can be anysuitable hardware processor or combination of processors, such as a CPU,a GPU, and so on. In some embodiments, the one or more data acquisitionsystems 824 are generally configured to acquire data, images, or both,and can include an MRI system. Additionally or alternatively, in someembodiments, one or more data acquisition systems 824 can include anysuitable hardware, firmware, and/or software for coupling to and/orcontrolling operations of an MRI system. In some embodiments, one ormore portions of the one or more data acquisition systems 824 can beremovable and/or replaceable.

Note that, although not shown, data source 702 can include any suitableinputs and/or outputs. For example, data source 702 can include inputdevices and/or sensors that can be used to receive user input, such as akeyboard, a mouse, a touchscreen, a microphone, a trackpad, a trackball,and so on. As another example, data source 702 can include any suitabledisplay devices, such as a computer monitor, a touchscreen, atelevision, etc., one or more speakers, and so on.

In some embodiments, communications systems 826 can include any suitablehardware, firmware, and/or software for communicating information tocomputing device 750 (and, in some embodiments, over communicationnetwork 754 and/or any other suitable communication networks). Forexample, communications systems 826 can include one or moretransceivers, one or more communication chips and/or chip sets, and soon. In a more particular example, communications systems 826 can includehardware, firmware and/or software that can be used to establish a wiredconnection using any suitable port and/or communication standard (e.g.,VGA, DVI video, USB, RS-232, etc.), Wi-Fi connection, a Bluetoothconnection, a cellular connection, an Ethernet connection, and so on.

In some embodiments, memory 828 can include any suitable storage deviceor devices that can be used to store instructions, values, data, or thelike, that can be used, for example, by processor 822 to control the oneor more data acquisition systems 824, and/or receive data from the oneor more data acquisition systems 824; to images from data; presentcontent (e.g., images, a user interface) using a display; communicatewith one or more computing devices 750; and so on. Memory 828 caninclude any suitable volatile memory, non-volatile memory, storage, orany suitable combination thereof. For example, memory 828 can includeRAM, ROM, EEPROM, one or more flash drives, one or more hard disks, oneor more solid state drives, one or more optical drives, and so on. Insome embodiments, memory 828 can have encoded thereon, or otherwisestored therein, a program for controlling operation of data source 702.In such embodiments, processor 822 can execute at least a portion of theprogram to generate images, transmit information and/or content (e.g.,data, images) to one or more computing devices 750, receive informationand/or content from one or more computing devices 750, receiveinstructions from one or more devices (e.g., a personal computer, alaptop computer, a tablet computer, a smartphone, etc.), and so on.

In some embodiments, any suitable computer readable media can be usedfor storing instructions for performing the functions and/or processesdescribed herein. For example, in some embodiments, computer readablemedia can be transitory or non-transitory. For example, non-transitorycomputer readable media can include media such as magnetic media (e.g.,hard disks, floppy disks), optical media (e.g., compact discs, digitalvideo discs, Blu-ray discs), semiconductor media (e.g., random accessmemory (“RAM”), flash memory, electrically programmable read only memory(“EPROM”), electrically erasable programmable read only memory(“EEPROM”)), any suitable media that is not fleeting or devoid of anysemblance of permanence during transmission, and/or any suitabletangible media. As another example, transitory computer readable mediacan include signals on networks, in wires, conductors, optical fibers,circuits, or any suitable media that is fleeting and devoid of anysemblance of permanence during transmission, and/or any suitableintangible media.

Referring particularly now to FIG. 9, an example of an MRI system 900that can implement the methods described here is illustrated. The MRIsystem 900 includes an operator workstation 902 that may include adisplay 904, one or more input devices 906 (e.g., a keyboard, a mouse),and a processor 908. The processor 908 may include a commerciallyavailable programmable machine running a commercially availableoperating system. The operator workstation 902 provides an operatorinterface that facilitates entering scan parameters into the MRI system900. The operator workstation 902 may be coupled to different servers,including, for example, a pulse sequence server 910, a data acquisitionserver 912, a data processing server 914, and a data store server 916.The operator workstation 902 and the servers 910, 912, 914, and 916 maybe connected via a communication system 940, which may include wired orwireless network connections.

The pulse sequence server 910 functions in response to instructionsprovided by the operator workstation 902 to operate a gradient system918 and a radiofrequency (“RF”) system 920. Gradient waveforms forperforming a prescribed scan are produced and applied to the gradientsystem 918, which then excites gradient coils in an assembly 922 toproduce the magnetic field gradients G_(x), G_(y), and G_(z) that areused for spatially encoding magnetic resonance signals. The gradientcoil assembly 922 forms part of a magnet assembly 924 that includes apolarizing magnet 926 and a whole-body RF coil 928.

RF waveforms are applied by the RF system 920 to the RF coil 928, or aseparate local coil to perform the prescribed magnetic resonance pulsesequence. Responsive magnetic resonance signals detected by the RF coil928, or a separate local coil, are received by the RF system 920. Theresponsive magnetic resonance signals may be amplified, demodulated,filtered, and digitized under direction of commands produced by thepulse sequence server 910. The RF system 920 includes an RF transmitterfor producing a wide variety of RF pulses used in MRI pulse sequences.The RF transmitter is responsive to the prescribed scan and directionfrom the pulse sequence server 910 to produce RF pulses of the desiredfrequency, phase, and pulse amplitude waveform. The generated RF pulsesmay be applied to the whole-body RF coil 928 or to one or more localcoils or coil arrays.

The RF system 920 also includes one or more RF receiver channels. An RFreceiver channel includes an RF preamplifier that amplifies the magneticresonance signal received by the coil 928 to which it is connected, anda detector that detects and digitizes the I and Q quadrature componentsof the received magnetic resonance signal. The magnitude of the receivedmagnetic resonance signal may, therefore, be determined at a sampledpoint by the square root of the sum of the squares of the I and Qcomponents:

M=√{square root over (I ² +Q ²)}  (3);

and the phase of the received magnetic resonance signal may also bedetermined according to the following relationship:

$\begin{matrix}{\phi = {{\tan^{- 1}( \frac{Q}{I} )}.}} & (4)\end{matrix}$

The pulse sequence server 910 may receive patient data from aphysiological acquisition controller 930. By way of example, thephysiological acquisition controller 930 may receive signals from anumber of different sensors connected to the patient, includingelectrocardiograph (“ECG”) signals from electrodes, or respiratorysignals from a respiratory bellows or other respiratory monitoringdevices. These signals may be used by the pulse sequence server 910 tosynchronize, or “gate,” the performance of the scan with the subject'sheart beat or respiration.

The pulse sequence server 910 may also connect to a scan room interfacecircuit 932 that receives signals from various sensors associated withthe condition of the patient and the magnet system. Through the scanroom interface circuit 932, a patient positioning system 934 can receivecommands to move the patient to desired positions during the scan.

The digitized magnetic resonance signal samples produced by the RFsystem 920 are received by the data acquisition server 912. The dataacquisition server 912 operates in response to instructions downloadedfrom the operator workstation 902 to receive the real-time magneticresonance data and provide buffer storage, so that data is not lost bydata overrun. In some scans, the data acquisition server 912 passes theacquired magnetic resonance data to the data processor server 914. Inscans that require information derived from acquired magnetic resonancedata to control the further performance of the scan, the dataacquisition server 912 may be programmed to produce such information andconvey it to the pulse sequence server 910. For example, duringpre-scans, magnetic resonance data may be acquired and used to calibratethe pulse sequence performed by the pulse sequence server 910. Asanother example, navigator signals may be acquired and used to adjustthe operating parameters of the RF system 920 or the gradient system918, or to control the view order in which k-space is sampled. In stillanother example, the data acquisition server 912 may also processmagnetic resonance signals used to detect the arrival of a contrastagent in a magnetic resonance angiography (“MRA”) scan. For example, thedata acquisition server 912 may acquire magnetic resonance data andprocesses it in real-time to produce information that is used to controlthe scan.

The data processing server 914 receives magnetic resonance data from thedata acquisition server 912 and processes the magnetic resonance data inaccordance with instructions provided by the operator workstation 902.Such processing may include, for example, reconstructing two-dimensionalor three-dimensional images by performing a Fourier transformation ofraw k-space data, performing other image reconstruction algorithms(e.g., iterative or backprojection reconstruction algorithms), applyingfilters to raw k-space data or to reconstructed images, generatingfunctional magnetic resonance images, or calculating motion or flowimages.

Images reconstructed by the data processing server 914 are conveyed backto the operator workstation 902 for storage. Real-time images may bestored in a data base memory cache, from which they may be output tooperator display 902 or a display 936. Batch mode images or selectedreal time images may be stored in a host database on disc storage 938.When such images have been reconstructed and transferred to storage, thedata processing server 914 may notify the data store server 916 on theoperator workstation 902. The operator workstation 902 may be used by anoperator to archive the images, produce films, or send the images via anetwork to other facilities.

The MRI system 900 may also include one or more networked workstations942. For example, a networked workstation 942 may include a display 944,one or more input devices 946 (e.g., a keyboard, a mouse), and aprocessor 948. The networked workstation 942 may be located within thesame facility as the operator workstation 902, or in a differentfacility, such as a different healthcare institution or clinic.

The networked workstation 942 may gain remote access to the dataprocessing server 914 or data store server 916 via the communicationsystem 940. Accordingly, multiple networked workstations 942 may haveaccess to the data processing server 914 and the data store server 916.In this manner, magnetic resonance data, reconstructed images, or otherdata may be exchanged between the data processing server 914 or the datastore server 916 and the networked workstations 942, such that the dataor images may be remotely processed by a networked workstation 942.

The present disclosure has described one or more preferred embodiments,and it should be appreciated that many equivalents, alternatives,variations, and modifications, aside from those expressly stated, arepossible and within the scope of the invention.

1. A method for constructing and implementing a machine learning modelto generate at least one image that depicts spatial patterns of amolecular marker across a region-of-interest in a subject, the steps ofthe method comprising: constructing a trained machine learning model by:(i) accessing training data with a computer system, the training datacomprising magnetic resonance imaging (MRI) data acquired from one ormore subjects and molecular data determined from biopsies collected fromthe one or more subjects, wherein the molecular data comprises DNA copynumber variation (CNV) data and exome data; (ii) quantifying regionalmolecular diversity in the one or more subjects from the molecular data;(iii) training a machine learning model based on the training data andthe quantified regional molecular diversity in the one or more subjects,wherein the machine learning model is trained on the training data tolocalize molecularly distinct subpopulations and phenotypic nichesacross a region-of-interest; and generating an image that depictsspatial patterns of a molecular marker across a region-of-interest in asubject by inputting magnetic resonance images acquired from the subjectto the trained machine learning model.
 2. The method as recited in claim1, wherein the region-of-interest includes a brain around tumor (BAT)region.
 3. The method as recited in claim 1, wherein the training datafurther comprises biological factor data including at least one of a sexor an age of each of the one or more subjects.
 4. The method as recitedin claim 3, wherein the biological factor data includes the sex of eachof the one or more subjects and step (iii) includes training a firstmachine learning model based the training data and the quantifiedregional molecular diversity in the one or more subjects associated witha female sex and training a second machine learning model based thetraining data and the quantified regional molecular diversity in the oneor more subjects associated with a male sex.
 5. The method as recited inclaim 4, wherein the image whose pixel values quantify regionalmolecular diversity in the region-of-interest in the subject isgenerated by inputting magnetic resonance images acquired from thesubject to the first machine learning model when the subject is a femaleand to the second machine learning model when the subject is a male. 6.The method as recited in claim 1, wherein the molecular data correspondsto a plurality of different molecular markers and step (iii) includestraining a different machine learning model for each of the plurality ofdifferent molecular markers based on the training data and thequantified regional molecular diversity in the plurality of subjectsassociated with each different molecular marker.
 7. The method asrecited in claim 6, wherein the molecular marker includes at least oneof EGFR, PDGFRA, PTEN, NF1, TP53, CDKN2A, RB1, ATRX, or MET.
 8. Themethod as recited in claim 1, wherein quantifying regional moleculardiversity in the plurality of subjects from the molecular data includesquantifying intra-biopsy heterogeneity.
 9. The method as recited inclaim 8, wherein intra-biopsy heterogeneity is quantified based on apresence of a molecular marker throughout biopsy sub-regions, an absenceof a molecular marker throughout biopsy sub-regions, or an admixture ofa molecular marker being present and absent throughout biopsysub-regions.
 10. The method as recited in claim 1, wherein quantifyingregional molecular diversity in the one or more subjects from themolecular data includes quantifying correlations between differentmolecular markers.
 11. The method as recited in claim 1, wherein themolecular data further comprises at least one of RNA sequencing(RNA-seq), gene expression, proteomics analysis data, or combinationsthereof.
 12. The method as recited in claim 11, wherein quantifyingregional molecular diversity in the one or more subjects from themolecular data includes classifying molecularly distinct subpopulationsbased on at least one of the CNV data and the exome data.
 13. The methodas recited in claim 11, wherein the molecular marker is a phenotypicmarker and quantifying regional molecular diversity in the one or moresubjects from the molecular data includes quantifying phenotypic nichesbased on RNA-seq.
 14. The method as recited in claim 13, wherein thephenotypic marker indicates at least one of hypoxia, cell proliferation,angiogenesis, cell invasion, or combinations thereof.
 15. The method asrecited in claim 1, wherein the MRI data comprises magnetic resonanceimages and parametric maps generated from the magnetic resonance images.16. The method as recited in claim 15, wherein the magnetic resonanceimages include at least one of T1-weighted images, post-contrastT1-weighted images, T2-weighted images, post-contrast T2-weightedimages, T2*-weighted images, post-contrast T2*-weighted images,diffusion-weighted images, perfusion-weighted images, or combinationsthereof.
 17. The method as recited in claim 15, wherein the parametricmaps include at least one of relative cerebral blood volume, meandiffusivity, fractional anisotropy, or combinations thereof.
 18. Themethod as recited in claim 15, wherein the MRI data further comprisestexture feature maps generated from at least one of the magneticresonance images or the parametric maps.
 19. The method as recited inclaim 18, wherein the texture feature maps include texture featurescomputed based on at least one of a gray-level co-occurrence matrix(GLCM), local binary patterns (LBP), a discrete orthonormal Stockwelltransform (DOST), or a Gabor filter.
 20. The method as recited in claim1, wherein the trained machine learning model is a transfer learningmodel trained on MRI data and molecular data acquired from the subject.21. The method as recited in claim 1, wherein quantifying regionalmolecular diversity in the one or more subjects from the molecular datacomprises quantifying the molecular diversity based on one or morecontinuous variables.
 22. A method for constructing and implementing amachine learning model to generate at least one image that depictsspatial patterns of a biomarker across a region-of-interest in asubject, the steps of the method comprising: constructing a trainedmachine learning model by: (i) accessing training data with a computersystem, the training data comprising imaging data acquired from one ormore subjects and biological feature data determined from biopsiescollected from the one or more subjects; (ii) quantifying regionalbiomarker diversity in the one or more subjects from the biologicalfeature data; (iii) training a machine learning model based on thetraining data and the quantified regional biomarker diversity in the oneor more subjects, wherein the machine learning model is trained on thetraining data to localize distinct subpopulations across aregion-of-interest; and generating an image that depicts spatialpatterns of a biomarker across a region-of-interest in a subject byinputting images acquired from the subject to the trained machinelearning model.
 23. The method as recited in claim 22, wherein thebiomarker comprises a histological feature.
 24. The method as recited inclaim 23, wherein the histological feature includes cell density. 25.The method as recited in claim 22, wherein the biomarker includes abiological descriptor of a tumor comprising at least one of tumoraggressiveness, tumor subtype, or likelihood of recurrence.
 26. Themethod as recited in claim 22, wherein the biomarker indicatesinteractions between the distinct subpopulations across theregion-of-interest.
 27. The method as recited in claim 26, wherein theinteractions between the distinct subpopulations comprise cellinteractions.
 28. The method as recited in claim 27, wherein thedistinct subpopulations comprise molecularly distinct subpopulations.29. The method as recited in claim 22, wherein the trained machinelearning model is a transfer learning model trained on imaging data andbiological feature data acquired from the subject.
 30. The method asrecited in claim 22, wherein quantifying regional biomarker diversity inthe one or more subjects from the biological feature data comprisesquantifying the biomarker diversity based on one or more continuousvariables.
 31. A method for constructing and implementing a machinelearning model to generate at least one image that depicts spatialpatterns of spatially resolved data across a spatial region, the stepsof the method comprising: constructing a trained machine learning modelby: (i) accessing training data with a computer system, the trainingdata comprising spatial data acquired from one or more spatial regionsand localized data collected from spatial samples in the one or morespatial regions; (ii) training a machine learning model based on thetraining data, wherein the machine learning model is trained on thetraining data to quantify spatial patterns of localized data across aspatial region; and generating an image that depicts spatial patterns oflocalized data across a spatial region by inputting spatially resolveddata acquired from the spatial region to the trained machine learningmodel.
 32. The method as recited in claim 31, wherein the localized datacomprise patient health data.
 33. The method as recited in claim 31,wherein the localized data comprise epidemiological data.
 34. The methodas recited in claim 31, wherein the spatial data are geographic datathat comprise satellite images.
 35. The method as recited in claim 31,wherein the spatial data are geographic data that comprise census data.36. The method as recited in claim 35, wherein the census data compriseat least one of census tracts, census block groups, or census blocks.37. The method as recited in claim 31, wherein the spatial data compriseorgan region data and the localized data comprise histological featuredata.
 38. A method for constructing and implementing a machine learningmodel to generate at least one image that depicts spatial patterns ofgenetic interactions across a region-of-interest in a subject, the stepsof the method comprising: constructing a trained machine learning modelby: (i) accessing training data with a computer system, the trainingdata comprising imaging data acquired from one or more subjects andbiological feature data determined from biopsies collected from the oneor more subjects; (ii) quantifying regional genetic interactions in theone or more subjects from the biological feature data; (iii) training amachine learning model based on the training data and the quantifiedregional genetic interactions in the one or more subjects, wherein themachine learning model is trained on the training data to localizedistinct subpopulations across a region-of-interest; and generating animage that depicts spatial patterns of a genetic interaction across aregion-of-interest in a subject by inputting images acquired from thesubject to the trained machine learning model